Material Measurement Units for a Circular Economy: Foundations through a Review
arXiv:2103.01997 · doi:10.1016/j.spc.2022.05.022
Abstract
Long-term availability of minerals and industrial materials is a necessary condition for sustainable development as they are the constituents of any manufacturing product. To enhance the efficiency of material management, we define a computer-vision-enabled material measurement system and provide a review of works relevant to its development with particular emphasis on the foundations. A network of such systems for wide-area material stock monitoring is also covered. Finally, challenges and future research directions are discussed. As the first article bridging industrial ecology and advanced computer vision, this review is intended to support both research communities towards more sustainable manufacturing.
arXiv commentExtension and overall improvement of previous version
References in corpus (10)
- GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
- Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
- Introduction to the Bag of Features Paradigm for Image Classification and Retrieval
- TACO: Trash Annotations in Context for Litter Detection
- A Multi-Level Approach to Waste Object Segmentation
- A Similarity Measure for Material Appearance
- Food Image Recognition by Using Convolutional Neural Networks (CNNs)
- An Adaptive Memory Multi-Batch L-BFGS Algorithm for Neural Network Training
- Lazy FSCA for Unsupervised Variable Selection
- ContamiNet: Detecting Contamination in Municipal Solid Waste